Vehicle license plate monitoring is used in a wide variety of traffic surveillance applications including traffic control, controlling access to supervised areas, and identifying stolen vehicles.
License Plate Recognition (LPR) is an image-processing technology used to identify vehicles by their license plates. As used herein, license plate and license plate number refer generally to the alphanumeric character string normally used on license plates. This technology is used in various security and traffic applications including location of stolen vehicles and access-control. LPR technology assumes that all vehicles have their identity displayed and that no additional transponder is required to be installed on the car. An LPR system uses illumination, such as infrared and a camera to take the image of the front or rear of the vehicle. Image-processing software then analyzes the images and extracts the plate information. This data is used for enforcement, data collection, and in access-control applications.
A complete LPR system normally contains the following parts: camera, illumination source, frame grabber, computer, software, hardware, and a database. The illumination source is a controlled light that can brighten up the license plate, and allow both day and night operation. In most cases, the illumination source is infrared, which is invisible to the driver. The frame grabber is an interface board between the camera and the computer, allowing the software to read the image information. The computer is normally a PC running the Windows or Linux operating systems. The computer processor executes the LPR application that controls the system, reads the images, analyzes and identifies the plate, and interfaces with other applications and systems. The software includes the LPR application recognition package. The hardware includes various input/output boards that are used to interface to the external world, such as control boards and networking boards. The database stores recorded events and can be a local database or a central database. The data recorded includes the recognition results.
In the image acquisition subsystem, a miniaturized digital camera is combined with a pulsed infrared LED illuminator that is synchronized with the camera aperture. There are multiple illumination alternatives that may be used in these sensor configurations. In some cases, it is appropriate to use a visible near-infrared LED light source. Using a short wavelength (as low as 735 nanometers) pulse, the illumination source appears as a flashing red light. In other covert applications, an alternative non-visible infrared LED light can be used. In these cases, a longer wavelength (up to 880 nanometers) pulse is used and effective when there is sufficient contrast between the plate characters and the plate background (typically with dark characters on a white or clear background).
The first image processing step is aimed at detecting the presence of any candidate plate from the continuous video flow. When a candidate plate is detected, the result of processing of the input image is definition of a region of interest that contains all of the relevant image features, i.e., discontinuities that may be indications of a plate's presence.
The normalized region of interest is further processed with a two-dimensional digital filter for contrast and edge enhancement to allow the identification and separation of each individual character with respect to the background of the plate. The result of this processing step is a sequence of rectangular boxes (segments) that contain all candidate characters and that may be aligned on a single line or multiple lines, if necessary.
The next step in the character recognition process is the measurement of each candidate's segment with respect to the “models” that have been acquired during a learning phase. This measurement process is based on a statistical technique of feature matching; all characters are described as a sequence of image features and a normalized distance is computed between each character sample and the stored feature models acquired from examples during the learning phase. This distance achieves a minimum value when the most similar character is found in the list of models.
The contextual analysis process then exploits both spatial and syntactic information in order to select the best hypothesis for the number plate. If the image being processed contains N validated characters on a number plate containing K characters, the general idea is to extract all choices of K elements from N and to evaluate them both spatially and syntactically.
The final temporal post-processing stage aims to extract a single number plate for each detected vehicle. This identification is obtained by tracking all recognized characters along the consecutive video frames. All number plate hypotheses that satisfy such tracking process are merged together if they are syntactically similar and are spatially coherent with the assumed vehicle trajectory in the image plane. The result of this temporal integration is an improved accuracy of the recognized plate and the possibility of recovering some character that may appear and disappear in the image during the transit of the plate (e.g., when the plate enters or exits the image frame). The temporal integration is run independently for all plate hypotheses so that multiple transit plates can be tracked and recognized simultaneously.